Industry PhD Projects

The Industry PhD Program is engaging industry and universities to train the future workforce through practical, demand driven research projects. Industry partners, in conjunction with academia, provide the Industry PhD research agenda based on their societal knowledge of pressing needs and issues.

RACE for 2030 will fully fund 55 industry PhD candidates to be completed between now and 2030 within the five RACE research programs:

  • RACE for Business
  • RACE for Homes
  • RACE for Networks
  • RACE for EVs
  • RACE for Change

RACE for 2030 will provide three years of funding at $38,000 per annum (tax exempt) to the candidates via a scholarship to the host University. Find out more about our Industry PhD program here.

Below, you will find out current project opportunities and read more about our current projects.

Current Projects

To examine how collaborative governance processes and practices can enable effective technology transfer and inclusive decision-making and foster leadership for integrity and longevity of NZPs.

Cities are facilitators of economic growth through agglomeration of economies from diversity of resources and population. This makes cities as the first responder to carbon emission strategies and decarbonisation effort due to high concentration of urban emission. In turn, precincts make cities and propels its growth – consequently capable to influence any policies related to decarbonisation.

The CRC RACE for 2030 Pathways to Net Zero Precincts (NZP) project has been awarded to Curtin University for a three year period and includes several PhD scholarships.

A highly competitive scholarship is available to support a suitably interested and eligible candidate to undertake a 3-year Industry PhD research project at University of Technology Sydney.

V2G V2H

The successful PhD scholarship applicant will explore the approach of Vehicle-to-Home and Vehicle-to-Grid System (V2H/V2G) using a Microgrid and energy storage-based EV Charging Systems.

hosting capacity

This PhD project on dynamic hosting capacity aims to develop an effective decision support framework based on artificial intelligence (AI)-based algorithms for modelling, analysing, and coordinating the behaviours of photovoltaic systems, storage batteries and electric vehicles to increase their penetration levels without breaching the network constraints and any extra investments. It will allow the industry partner to integrate the proposed tool with their existing software platforms.

DERs

The PhD student will investigate how new customer transaction and business models can be used to accelerate the uptake of Distributed Energy Resources (DERs) in order to cost effectively assist in stabilising the grid during the transition to decentralised energy generation and storage.

Renweable energy

Water corporations have extensive electricity demands, which lead to high energy bills and significant carbon emissions.

AD

Biogas can be produced from anaerobic digestion (AD) of agricultural wastes such as sugarcane bagasse, mill mud and chicken manure.

optimisation

Traditional chiller equipment operation optimisation focuses on short term gains. However, this can be at the detriment to long term equipment lifecycle cost.

Load flexibility will address challenges of the evolving power system in Australia, allowing increased amounts of variable renewable energy to be integrated.

Households are taking control of their energy supply through solar PV systems and increasingly, battery storage and EVs. This Industry PhD project will investigate the role of green hydrogen in the home to supplement these systems and how it can be applied in the Western Australian context. The research questions for this project will investigate the technical, regulatory and social aspects of deploying this energy source in households, in a small scale and possibly portable manner.

Explore opportunities for dynamic optimisation of energy use in large buildings, responding to weather, building operational data, electricity prices and emissions intensity data to help minimise carbon emissions, reduce energy costs and support the electricity distribution network.

This PhD project is a unique opportunity to contribute to both theory and practice as the student will work closely with in the energy sector.

The research is focused on recommended design and engineering principles of totally renewable microgrids with the ambition to outperform traditional networks reliability characteristics, and identify new ways of defining reliability in the context of autonomous microgrids.

This project aims to develop and implement intelligent techniques to manage the charging and discharging of electric vehicle (EV) batteries and design EV management framework using state of the art artificial intelligence algorithms for addressing major challenges that arise in the deployment and management of EVs in residential and commercial levels.

A successful clean energy transition requires customers to be central in creating and using clean energy products.

A successful clean energy transition requires customers to be central in creating and using clean energy products.

The scholarship will support research in data science and machine learning, with a focus on commercial building demand forecasting and load optimisation in a dynamic operating environment with potentially conflicting environmental, cost and health objectives.

The scholarship will support research in data science and machine learning, with a focus on commercial building demand forecasting and load optimisation in a dynamic operating environment with potentially conflicting environmental, cost and health objectives.

This project will focus on biogas production in a WWTP, and an optimal design strategy will be developed to maximize its revenues and usage. This research is primarily aimed to assist Sydney Water Corporation with problems they are facing, but this methodology can be used for others water supply companies.

This project aims to develop and implement intelligent techniques to manage the charging and discharging of electric vehicle (EV) batteries and design EV management framework using state of the art artificial intelligence algorithms for addressing major challenges that arise in the deployment and management of EVs in residential and commercial levels.